Incorporating inventory and routing costs in strategic location models

We consider a supply chain design problem where the decision maker needs to decide the number and locations of the distribution centers (DCs). Customers face random demand, and each DC maintains a certain amount of safety stock in order to achieve a certain service level for the customers it serves....

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Bibliographic Details
Published in:European journal of operational research Vol. 179; no. 2; pp. 372 - 389
Main Authors: Max Shen, Zuo-Jun, Qi, Lian
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.06.2007
Elsevier
Elsevier Sequoia S.A
Series:European Journal of Operational Research
Subjects:
Applied sciences
Cost control
Decision theory. Utility theory
Distribution centers
Distribution costs
Exact sciences and technology
Integer programming
Integrated supply chain design models
Inventory
Lagrange multiplier
Location models
Logistics
Nonlinear programming
Operational research and scientific management
Operational research. Management science
Optimization algorithms
Studies
Supply chain management
Supply chains
Vehicle routing
Integrated supply chain design models
Location models
Vehicle routing
Inventory
Distribution cost
Routing
Non linear programming
Polynomial method
Safety stock
Optimization
Integer programming
Lagrange multiplier
User service
Non linear model
Integrated design
Relaxation method
Localization
Logistics
ISSN:0377-2217, 1872-6860
Online Access:Get full text
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Summary:We consider a supply chain design problem where the decision maker needs to decide the number and locations of the distribution centers (DCs). Customers face random demand, and each DC maintains a certain amount of safety stock in order to achieve a certain service level for the customers it serves. The objective is to minimize the total cost that includes location costs and inventory costs at the DCs, and distribution costs in the supply chain. We show that this problem can be formulated as a nonlinear integer programming model, for which we propose a Lagrangian relaxation based solution algorithm. By exploring the structure of the problem, we find a low-order polynomial algorithm for the nonlinear integer programming problem that must be solved in solving the Lagrangian relaxation sub-problems. We present computational results for several instances of the problem with sizes ranging from 40 to 320 customers. Our results show the benefits of having an integrated supply chain design framework that includes location, inventory, and routing decisions in the same optimization model.
Bibliography:SourceType-Scholarly Journals-1
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ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2006.03.032